


'''simple docstring'''


import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
				import os

				from flax.core.frozen_dict import unfreeze
				from flax.traverse_util import flatten_dict

				from transformers import FlaxBertModel

				UpperCAmelCase_						:  List[str]				  =						'0.12'  # assumed parallelism: 8





@require_flax
@is_staging_test
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@classmethod
							def UpperCAmelCase_      (       cls    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= TOKEN
											HfFolder.save_token(__snake_case    )






							@classmethod
							def UpperCAmelCase_      (       cls    ):
											try:
															delete_repo(token=cls._token       ,   repo_id="""test-model-flax"""    )
											except HTTPError:
															pass

											try:
															delete_repo(token=cls._token       ,   repo_id="""valid_org/test-model-flax-org"""    )
											except HTTPError:
															pass






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= BertConfig(
											    vocab_size=99       ,   hidden_size=32       ,   num_hidden_layers=5       ,   num_attention_heads=4       ,   intermediate_size=37    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= FlaxBertModel(__snake_case    )
											model.push_to_hub("""test-model-flax"""       ,   use_auth_token=self._token    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax"""    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= flatten_dict(unfreeze(model.params    )    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= flatten_dict(unfreeze(new_model.params    )    )

											for key in base_params.keys():
															_SCREAMING_SNAKE_CASE					:			int							= (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case       ,   1e-3       ,   msg=f"""{key} not identical"""    )

											# Reset repo
											delete_repo(token=self._token       ,   repo_id="""test-model-flax"""    )

											# Push to hub via save_pretrained
											with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(__snake_case       ,   repo_id="""test-model-flax"""       ,   push_to_hub=__snake_case       ,   use_auth_token=self._token    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax"""    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= flatten_dict(unfreeze(model.params    )    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= flatten_dict(unfreeze(new_model.params    )    )

											for key in base_params.keys():
															_SCREAMING_SNAKE_CASE					:			List[str]							= (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case       ,   1e-3       ,   msg=f"""{key} not identical"""    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= BertConfig(
											    vocab_size=99       ,   hidden_size=32       ,   num_hidden_layers=5       ,   num_attention_heads=4       ,   intermediate_size=37    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= FlaxBertModel(__snake_case    )
											model.push_to_hub("""valid_org/test-model-flax-org"""       ,   use_auth_token=self._token    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= flatten_dict(unfreeze(model.params    )    )
											_SCREAMING_SNAKE_CASE					:			Any							= flatten_dict(unfreeze(new_model.params    )    )

											for key in base_params.keys():
															_SCREAMING_SNAKE_CASE					:			Any							= (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case       ,   1e-3       ,   msg=f"""{key} not identical"""    )

											# Reset repo
											delete_repo(token=self._token       ,   repo_id="""valid_org/test-model-flax-org"""    )

											# Push to hub via save_pretrained
											with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(
															    __snake_case       ,   repo_id="""valid_org/test-model-flax-org"""       ,   push_to_hub=__snake_case       ,   use_auth_token=self._token    )

											_SCREAMING_SNAKE_CASE					:			str							= FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""    )

											_SCREAMING_SNAKE_CASE					:			int							= flatten_dict(unfreeze(model.params    )    )
											_SCREAMING_SNAKE_CASE					:			str							= flatten_dict(unfreeze(new_model.params    )    )

											for key in base_params.keys():
															_SCREAMING_SNAKE_CASE					:			Tuple							= (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case       ,   1e-3       ,   msg=f"""{key} not identical"""    )







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= True
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= flatten_dict(modela.params	)
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= flatten_dict(modela.params	)
				for key in flat_params_a.keys():
								if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]	)	) > 1E-4:
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False

				return models_are_equal







@require_flax
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= FlaxBertModel(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= """bert"""
											with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case       ,   __snake_case    )    )

															with self.assertRaises(__snake_case    ):
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= FlaxBertModel.from_pretrained(__snake_case    )

															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= FlaxBertModel.from_pretrained(__snake_case       ,   subfolder=__snake_case    )

											self.assertTrue(check_models_equal(__snake_case       ,   __snake_case    )    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= FlaxBertModel(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= """bert"""
											with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case       ,   __snake_case    )       ,   max_shard_size="""10KB"""    )

															with self.assertRaises(__snake_case    ):
																			_SCREAMING_SNAKE_CASE					:			Tuple							= FlaxBertModel.from_pretrained(__snake_case    )

															_SCREAMING_SNAKE_CASE					:			Optional[int]							= FlaxBertModel.from_pretrained(__snake_case       ,   subfolder=__snake_case    )

											self.assertTrue(check_models_equal(__snake_case       ,   __snake_case    )    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """bert"""
											_SCREAMING_SNAKE_CASE					:			Tuple							= """hf-internal-testing/tiny-random-bert-subfolder"""

											with self.assertRaises(__snake_case    ):
															_SCREAMING_SNAKE_CASE					:			Dict							= FlaxBertModel.from_pretrained(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			int							= FlaxBertModel.from_pretrained(__snake_case       ,   subfolder=__snake_case    )

											self.assertIsNotNone(__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= """bert"""
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
											with self.assertRaises(__snake_case    ):
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= FlaxBertModel.from_pretrained(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Dict							= FlaxBertModel.from_pretrained(__snake_case       ,   subfolder=__snake_case    )

											self.assertIsNotNone(__snake_case    )








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




